Can data predict fashion trends?

IN the film “The Devil Wears Prada”, the character of Miranda Priestly, whose role is
based on a feared Vogue editor, scolds her new assistant for
not understanding fashion. Fashion, she tells her, is whatever a select group
of designers and critics says it is. What she does not say, however, is that
their judgments are themselves often influenced by another group: fashion
forecasters, who predict what will be “in”. Might these seers of style in turn
be undone by artificial intelligence (AI)?

Fashion
forecasting has always been a peculiar profession. The business came into its
own in Paris in the 1960s when agencies began releasing “trend books”,
collections of fabrics and design ideas. Retailers use these books for
inspiration as they put together designs.

The biggest of these forecasting firms is WGSN, with a market share of
50%. It employs 150 forecasters who scour the world’s catwalks, bars and clubs
to spot the next big thing. Their findings are then combined with other data,
from economic indicators to political sentiment. Petah Marian, a senior editor
at WGSN, is confident that the methodology works. She says her colleagues often
exclaim “I forecast that!” when visiting clothing shops.

Ms Marian’s confidence may seem surprising, given the lack of clear
correlations between fashion and macroeconomic data. Not much evidence supports
the theory of George Taylor, an economist, that hemlines rise with stocks, and
Leonard Lauder’s suggestion that lipstick sales increase during a downturn. Even
the co-founder of WGSN, Marc Worth, who sold the firm to set up a rival
service, once stated: “Nobody can really predict or forecast trends.” If
forecasters can claim accuracy rates of up to 80%, it is because their
predictions are often self-fulfilling. Most major retailers buy trend books.
For designers, they are a form of insurance: as long as they are widely used,
the risk of being wildly out of step with the market is modest.

The business of forecasting is menaced by data-driven analysis, however.
The clothing industry’s supply chain is becoming more digital and more
flexible: Inditex and H&M, for example, aim to take an idea and turn it
into a finished product ready for mass production in two weeks. In response,
forecasting agencies are making use of data collated from retailers’ IT systems
and have added short-term predictions to their portfolio of services. In 2013
WGSN launched INstock, a retail-analytics service, which uses past sales
figures to predict upcoming bestsellers. EDITED, a competing service, provides
“solid metrics” in fashion, claiming to use machine learning, an AI technique,
in order to predict short-term sales trends.

Such offerings notwithstanding, the marriage of AI and fashion is still
in its infancy. A study in 2014 found that the best predictive models get it
wrong nearly half the time. But forecasters are likely to face rising
competition as technology firms enter the market. Google, an online giant, now
has a “Trendspotting” division.

It releases a regular "Fashion Trends Report” based on the firm’s
vast trove of search data. So far the results are basic: in 2016 slim “mom
jeans” were on the rise while baggier “boyfriend jeans” were on the way out.
But Olivier Zimmer, the project’s data scientist, says that the goal is to
produce more sophisticated combinations of search and other data.

Whether AI will ever truly replace the woolly methods of fashion
forecasting remains to be seen. Some worry that using AI may dull design. The
business has already become “pedantic” and a matter of percentages, says
Michael Bennett, a former forecaster. But Julie King, a fashion expert at the
University of Northampton, expects the ingenuity of exciting couturiers to
prevail over the homogeneity of data-driven algorithms. If so, the Miranda
Priestlys of the world won’t stop dictating what’s hot and what’s not.